Ensuring data integrity is basic necessity or back bond in big data processing environment to achieve accurate outcome. Of course, same is applicable while executing any data moving operations with traditional data storage systems (RDBMS, Document Repository etc) through various applications. Data transportation over the network, device to device transfer, ETL process, and many more. In two words, data integrity can be defined as assurance of the accuracy and consistency of data throughout the entire life cycle.
In big data processing environment, data(rest) gets persisted in a distributed manner because of huge volume. So, achieving data integrity on top of it is challenging. Hadoop Distributed File Systems (HDFS) has been efficiently built/developed to store any type of data in a distributed manner in the form of data block (breaks down huge volume of data into a set of individual blocks) with data integrity commitment. There might be multiple reasons to get corrupt data blocks in HDFS, starting from IO operation on the system disk, network failure etc.
Can be contacted for real time POC development and hands-on technical training. Also to develop/support any Hadoop related project. Email:- [email protected] Gautam is a consultant as well as Educator. Prior to that, he worked as Sr. Technical Architect in multiple technologies and business domain. Currently, he is specializing in Big Data processing and analysis, Data lake creation, architecture etc. using HDFS. Besides, involved in HDFS maintenance and loading of multiple types of data from different sources, Design and development of real time use case development on client/customer demands to demonstrate how data can be leveraged for business transformation, profitability etc. He is passionate about sharing knowledge through blogs, seminars, presentations etc. on various Big Data related technologies, methodologies, real time projects with their architecture /design, multiple procedure of huge volume data ingestion, basic data lake creation etc.